2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec)

Increasing attention is given to finding ways for combining, integrating and mediating heterogeneous sources of information for the purpose of providing better personalized services in many information seeking and e-commerce applications. Information heterogeneity can indeed be identified in any of the pillars of a recommender system: the modeling of user preferences, the description of resource contents, the modeling and exploitation of the context in which recommendations are made, and the characteristics of the suggested resource lists.

Thus, in recommender systems, among other issues: a) user models could be based on different types of explicit and implicit personal preferences, such as ratings, tags, textual reviews, records of views, queries, and purchases; b) recommended resources may belong to several domains and media, and may be described with multilingual metadata; c) context could be modeled and exploited in multi-dimensional feature spaces; d) and ranked recommendation lists could be diverse according to particular user preferences and resource attributes, oriented to groups of users, and driven by multiple user evaluation criteria.

In the HetRec workshop, we raised awareness of the potential of using multiple sources of information, and looked for sharing expertise and suitable models and techniques. Another dire need was for strong datasets, and one of our aims was to establish benchmarks and standard datasets on which the problems could be investigated. In this edition, we made available on-line datasets with heterogeneous information from several social systems. These datasets could been used by participants to experiment and evaluate their recommendation approaches, enriched with additional data.